Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/agent-engineer-master/skill-engineer/analysis-quality-reviewnpx skills add Agent-Engineer-Master/skill-engineer --skill analysis-quality-reviewgit clone --depth 1 https://github.com/Agent-Engineer-Master/skill-engineerWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/agent-engineer-master/skill-engineer/analysis-quality-review)<a href="https://agentmods.dev/skills/agent-engineer-master/skill-engineer/analysis-quality-review"><img src="https://agentmods.dev/badge/skills/agent-engineer-master/skill-engineer/analysis-quality-review.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00462 | $0.07097 |
| Opus 5 | $0.00231 | $0.03549 |
| Sonnet 5 | $0.00092 | $0.01419 |
| Haiku 4.5 | $0.00046 | $0.00710 |
Grade C, and why
analysis-quality-review scanned grade C with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Hidden instructionshighPrompt injection
Directives inside HTML comments, invisible characters or bidirectional overrides are read by the model and not by the person reviewing the file.
<!-- Built with Agent Engineer Master — get your own production-ready skill: www.agentengineermaster.com/skill-engineer --> How it starts
The opening of the file, as written. The whole thing — 375 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Write Report (Analysis Quality Review) — Review-Only Multi-Mode Grader
This is a protocol skill. It grades reader-facing deliverables and ghost-deck storylines. It does NOT edit them. Each invocation runs a single mode and returns a structured response. The calling skill — which has the substantive context that produced the document — writes the spec itself (using a template this skill provides) and applies fixes itself (using references/fix-patterns.md), then re-invokes this skill to verify the fixes landed.
Input convention. Reader-facing deliverables are typically HTML (combined reports with prose + charts/diagrams inline) at standard+ strictness. Markdown is acceptable for working drafts at low strictness. The rubrics and reviewers grade content (argument structure, prose readability) regardless of file format — HTML chrome is ignored, prose and section structure are what's graded. Machine artifacts (YAML datasets, JSON, machine-readable manifests) should NOT be reviewed — they are audit substrate, not reader-facing.
The skill is built on four agents and one mechanism:
specmode (no agent): returns the framework-matched authoring template path. File lookup only.- Pre-flight (review modes only):
artifact-loader(runs once per review audit, buildsload_bearing_index.yaml) spec-judgemode:spec-judgeagent — grades the filled-in authoring spec against the declaredstructural_frameworkreview pass: 1:argument-structure-reviewer— grades document structure against the declaredstructural_frameworkreview pass: 2:readability-reviewer— grades document readability (framework-independent — applies to any prose document)
Structural frameworks — pick one per document
Analytical documents don't all use the same structural skeleton. The skill grades against ONE primary framework per document, declared by the caller via structural_framework. Frameworks are not composable; pick the one whose dominant frame matches the document's intent. (Nested Minto-style checks fire inside rumelt-kernel and issue-tree where they apply — handled by the rubric, not by composing frameworks.)
What ships with it
25 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/argument-structure-reviewer.md 12 KB
- agents/artifact-loader.md 5.7 KB
- agents/readability-reviewer.md 16 KB
- agents/storyline-judge.md 15 KB
- evals/evals.json 5.9 KB
- references/applicability-matrix.md 7.2 KB
- references/authoring-templates/adr.md 3.3 KB
- references/authoring-templates/concept-page.md 2.3 KB
- references/authoring-templates/descriptive.md 5.5 KB
- references/authoring-templates/issue-tree.md 5.5 KB
- references/authoring-templates/minto-pyramid.md 7.9 KB
- references/authoring-templates/rumelt-kernel.md 8.3 KB
- references/authoring-templates/scqa-only.md 3.5 KB
- references/calling-contract.md 16 KB
- references/deeper-reading.md 9.4 KB
- references/edge-cases.md 2.8 KB
- references/fix-patterns.md 27 KB
- references/framework-selection-guide.md 8.5 KB
- references/learnings.md 969 B
- references/load-bearing-architecture.md 7.6 KB
- references/rubric-readability.md 12 KB
- references/rubric-structure.md 11 KB
- references/spec-judge-rubric.md 12 KB
- references/tone-of-voice.md 12 KB
- references/worked-examples.md 19 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 375 lines · 462 tokens per session scan C d145439de753
analysis-quality-review is a skill published in the GitHub repository Agent-Engineer-Master/skill-engineer (8 stars, last pushed 1mo ago), licensed MIT. It adds 462 tokens to every session and 7,097 once invoked, about $0.0023 per session on Opus 5. A static security scan graded it C with 1 finding (hidden instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
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openspec-new-change
Start a new OpenSpec change using the experimental artifact workflow. Use when the user wants to create a new feature, fix, or modification with a structured step-by-step approach.
writing
将共享历史中的已验证事实和计算结果整理成符合受众、格式与长度约束的成稿。.
extract
Run the full Semantica semantic extraction pipeline on a file or selected text — NER, relations, events, coreference resolution, triplets, and validation. Clears result cache before each run. Returns Markdown tables with entity/relation/event/triplet results and inline validator warnings.
gsd-audit-milestone
Audit milestone completion against original intent before archiving.
kayba-stage-7-fixer
Implement the approved fixes from the action plan and log all changes. Trigger when the user says "run stage 7", "implement fixes", "apply action plan", or when invoked by the kayba-pipeline orchestrator. Requires eval/actionplan.md to exist.